Wideband interference mitigation for devices with multiple receivers

ABSTRACT

Certain disclosed embodiments pertain to suppressing interference in a wireless communication system. For example, a method of suppressing interference can include receiving one or more first signals including components from a plurality of sub-channels. Each of the first signals can be converted into a respective plurality of first sub-band frequency components. A respective spatial filter can be determined for each frequency sub-band using one or more corresponding first sub-band components for each respective spatial filter. One or more second signals including components from the plurality of sub-channels can be received. Each of the second signals can be converted into a respective plurality of second sub-band frequency components. A corresponding plurality of filtered sub-band components can be generated by applying the respective spatial filters to the corresponding second sub-band components for each of the second signals.

INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS

Any and all applications for which a foreign or domestic priority claimis identified in the Application Data Sheet as filed with the presentapplication are incorporated by reference under 37 CFR 1.57 and made apart of this specification.

BACKGROUND

1. Field

This disclosure relates generally to communication systems and, moreparticularly, to systems, methods and devices for mitigatinginterference in a communication system.

2. Description of Related Art

Wireless communication systems are widely deployed to provide varioustypes of communication (e.g., voice, data, multimedia services, etc.) tomultiple users. As the demand for various types of wirelesscommunication grows, there lies a challenge to implement efficient androbust communication systems. Wireless communication is made difficultby various factors that corrupt transmitted signals, such as forexample, the presence of noise, multipath fading and interferingsignals.

Certain existing communications system standards suffer from variousdrawbacks such as, for example, a lack of effective and constructivemethods for compensating for interference or certain types of noise. Inparticular, the unlicensed nature of the ISM (Industrial, Scientific,and Medical) bands has allowed for rapid development of various wirelesscommunication technologies and standards such as Bluetooth, which usesthe 2450 MHz ISM band, and IEEE 802.11, which uses the 2450 and 5800 MHzISM bands. Because communication devices using the ISM bands encounterinterference from other equipment, these bands are typically given overto uses intended for unlicensed operation, since unlicensed operationtypically needs to be tolerant of interference from other devicesanyway. Interference becomes an issue as devices are allowed to operatein the same band without pre-determined frequency, temporal, or spatialplanning.

There have been several attempts to mitigate this issue via higher layerprotocols. For example, methods involving cooperative scheduling havebeen implemented to turn portions of the random access channel into acontrolled access channel. Also, some work has been done to show thattime domain signal processing can be used to mitigate the effects ofnarrowband interference. However, the time domain methods have primarilyfocused on mitigating interference on the data payload, without takinginto account how interference affects other parts of the receiver.

SUMMARY

Various embodiments of systems, methods and devices within the scope ofthe appended claims each have several aspects, no single one of which issolely responsible for the desirable attributes described herein.Without limiting the scope of the appended claims, some prominentfeatures are described herein.

One aspect of the disclosure is a method of suppressing interference ina wireless communication system, the method comprising: receiving one ormore first signals on a frequency band, wherein each of the firstsignals comprises components from a plurality of sub-channels;converting each of the first signals into a respective plurality offirst sub-band frequency components, wherein each sub-band is defined inthe frequency domain; determining a respective spatial filter for eachfrequency sub-band using one or more corresponding first sub-bandcomponents for each respective spatial filter; receiving one or moresecond signals on said frequency band, wherein each of the secondsignals comprises components from the plurality of sub-channels;converting each of the second signals into a respective plurality ofsecond sub-band frequency components; and generating a correspondingplurality of filtered sub-band components by applying the respectivespatial filters to the corresponding second sub-band components for eachof the second signals.

In one embodiment, one or more first signals comprises at least twofirst signals, wherein the first signals are received from acorresponding plurality of receiver antennas, and each of the firstsignals comprises components from corresponding sub-channels between theplurality of receiver antennas and a plurality of transmitter antennas.

In one embodiment, wherein one or more first signals comprises a singlefirst signal, wherein the first signal is received from a correspondingsingle receiver antenna, and the first signal comprises components fromcorresponding sub-channels between the single receiver antenna and aplurality of transmitter antennas.

In one embodiment, wherein one or more first signals comprises at leasttwo first signals, wherein the first signals are received from acorresponding plurality of receiver antennas, and each of the firstsignals comprises components from corresponding sub-channels between theplurality of receiver antennas and a single transmitter antenna.

Another aspect of the disclosure is a machine readable medium havingmachine executable instructions stored thereon, that when executed by acomputing device are configured to: receive one or more first signals ona frequency band, wherein each of the first signals comprises componentsfrom a plurality of sub-channels; convert each of the first signals intoa respective plurality of first sub-band frequency components, whereineach sub-band is defined in the frequency domain; determine a respectivespatial filter for each frequency sub-band using one or morecorresponding first sub-band components for each respective spatialfilter; receive one or more second signals on said frequency band,wherein each of the second signals comprises components from theplurality of sub-channels; convert each of the second signals into arespective plurality of second sub-band frequency components; andgenerate a corresponding plurality of filtered sub-band components byapplying the respective spatial filters to the corresponding secondsub-band components for each of the second signals.

Another aspect of the disclosure is a device configured to suppressinterference in a wireless communication system, the device comprising:at least one input configured to receive at least one signal on afrequency band, wherein each signal comprises components from aplurality of sub-channels; at least one filter configured to convertsaid at least one signal from the time domain into a correspondingplurality of sub-band frequency components, wherein each sub-band isdefined in the frequency domain; a controller configured to executecode; and non-transitory computer readable memory storing code that whenexecuted by the controller is configured to: determine a respectivespatial filter for each frequency sub-band using one or more firstsub-band components for each respective spatial filter, wherein the oneor more first sub-band components are produced by the at least onefilter from a corresponding one or more first signals received at the atleast one input; and apply the respective spatial filter to one or moresecond sub-band components to produced filtered sub-band components,wherein the one or more second sub-band components are produced by theat least one filter from a corresponding one or more second signalsreceived at the at least one input.

Another aspect of the disclosure is a method of suppressing interferencein a wireless communication system, the method comprising: receiving afirst signal on a frequency band, wherein the first signal comprisescomponents from a plurality of sub-channels; converting the first signalinto a respective plurality of first sub-band frequency components,wherein each sub-band is defined in the frequency domain; determining arespective spatial filter for each frequency sub-band usingcorresponding first sub-band components for each respective spatialfilter; receiving a second signal on said frequency band, wherein thesecond signal comprises components from the plurality of sub-channels;converting the second signal into a respective plurality of secondsub-band frequency components; and generating a corresponding pluralityof filtered sub-band components by applying the respective spatialfilters to the corresponding second sub-band components.

Another aspect of the disclosure is a device for suppressinginterference in a wireless communication system, the device comprising:means for receiving one or more first signals on a frequency band,wherein each of the first signals comprises components from a pluralityof sub-channels; means for converting each of the first signals into arespective plurality of first sub-band frequency components, whereineach sub-band is defined in the frequency domain; means for determininga respective spatial filter for each frequency sub-band using one ormore corresponding first sub-band components for each respective spatialfilter; means for receiving one or more second signals on said frequencyband, wherein each of the second signals comprises components from theplurality of sub-channels; means for converting each of the secondsignals into a respective plurality of second sub-band frequencycomponents; and means for generating a corresponding plurality offiltered sub-band components by applying the respective spatial filtersto the corresponding second sub-band components for each of the secondsignals.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other sample aspects of the disclosure will be described inthe detailed description and the appended claims that follow, and in theaccompanying drawings.

FIG. 1 is a simplified block diagram of a wireless communication networkwith an interferer.

FIG. 2 is a flowchart illustrating a method of mitigating interferencein a wireless communication network.

FIG. 3 is a block diagram of a receiver in a wireless communicationnetwork.

FIG. 4 is a state diagram illustrating a method of controlling aninterference mitigation module.

FIGS. 5A-5L are plots of the simulated signal-to-noise-plus-interferenceratio (SINR) gain of various channels using a various interferencemitigation methods.

FIGS. 6A-6F are plots of the simulated packet detection performance ofvarious channels using a number of interference mitigation methods.

FIGS. 7A-7C are plots of the simulated packet detection performance ofvarious channels in wideband noise using an eigen nulling interferencemitigation method.

FIGS. 8A-8I are plots of the simulated packet detection performance onvarious channels with various numbers of transmitter antennas.

FIGS. 9A-9F are flowcharts illustrating various methods of mitigatinginterference in a wireless communication network.

FIG. 10 is a plot of overlapping DFT operations used for sub-banding andfiltering.

FIGS. 11A-C are simplified block diagrams of various embodiments of amulti-antenna interference mitigation system.

FIG. 12 is a block diagram of an embodiment of a filter-bank system.

FIG. 13 is a plot of the frequency response of various windows.

FIGS. 14A-14C are plots of eigen-value ratios with various windows.

FIG. 15 is a plot of the simulated signal-to-noise-plus-interferenceratio (SINR) gain of a channel using various interference mitigationfilters.

FIGS. 16A-16E are plots of the simulated packet detection performancewith various filters, windows, and spatial streams.

FIG. 17 is a plot of simulated packet detection performance with andwithout filtering.

FIG. 18 is a plot of hardware performance of an embodiment of aninterference mitigation system.

FIG. 19 is a block diagram of an embodiment of a system configured tomitigate interference in a wideband multi-antenna system.

FIG. 20 is a flowchart illustrating a method of implementing an overlapand add algorithm for frequency domain interference cancellation.

FIG. 21 is a flowchart illustrating a method of estimating frequencydomain interference autocorrelation matrices.

FIG. 22 is a flowchart illustrating a method of computing frequencydomain interference mitigation matrices based on a noise whiteningapproach.

FIGS. 23A-23D are plots of the simulated packet detection performance ofa frequency domain interference mitigation system in comparison to timedomain interference mitigation.

In accordance with common practice the various features illustrated inthe drawings may not be drawn to scale. Accordingly, the dimensions ofthe various features may be arbitrarily expanded or reduced for clarity.In addition, some of the drawings may be simplified for clarity. Thus,the drawings may not depict all of the components of a given apparatus(e.g., device) or method. Finally, like reference numerals may be usedto denote like features throughout the specification and figures.

DETAILED DESCRIPTION

Although certain preferred embodiments and examples are disclosed below,inventive subject matter extends beyond the specifically disclosedembodiments to other alternative embodiments and/or uses and tomodifications and equivalents thereof. Thus, the scope of the claimsthat may arise herefrom is not limited by any of the particularembodiments described below. For example, in any method or processdisclosed herein, the acts or operations of the method or process may beperformed in any suitable sequence and are not necessarily limited toany particular disclosed sequence. Various operations may be describedas multiple discrete operations in turn, in a manner that may be helpfulin understanding certain embodiments; however, the order of descriptionshould not be construed to imply that these operations are orderdependent. Additionally, the structures, systems, and/or devicesdescribed herein may be embodied as integrated components or as separatecomponents. For purposes of comparing various embodiments, certainaspects and advantages of these embodiments are described. Notnecessarily all such aspects or advantages are achieved by anyparticular embodiment. Thus, for example, various embodiments may becarried out in a manner that achieves or optimizes one advantage orgroup of advantages as taught herein without necessarily achieving otheraspects or advantages as may also be taught or suggested herein.

As discussed above, wireless communications is made difficult by variousfactors, such as for example, the presence of noise, multipath fadingand interfering signals. Signals received by a communication device canbe modeled as having two components: 1) a signal of interest and 2)other signals, including noise and interference. The signal of interestincludes information a transmitter transmits to a receiver. The rest ofthe signal includes noise and/or interference that make it difficult todiscern the signal-of-interest. There are various types of interference.For example, certain types of interference are the result transmissionfrom other devices, or natural phenomena that emit electromagneticradiation in a frequency band used for communication. Interference canalso be created by multiple reflections of a desired signal arriving ata receiver at different times. Those skilled in the art will appreciatethat there are numerous types of interference and an enumeration of eachtype of interference has not been provided herein for the sake ofbrevity.

FIG. 1 is a block diagram of a wireless communication network with aninterferer (or jammer). The wireless communication network 100 comprisesthree communication devices 110, 120, 130 in data communication witheach other over a number of channels 111, 112, 113. When the firstdevice 110, labeled Device A, transmits information to the second device120, labeled Device B, over a wireless channel 112, the second device120 receives both the transmitted information and noise. Noise sourcesin wireless data communication include, but are not limited to,background radiation, thermal noise, electronic noise, etc. The seconddevice 120 can also receive a signal from an interferer 140 over anotherwireless communication channel 142. The interferer 140 may, for example,be attempting to transmit information to the third device 130, labeledDevice C, be transmitting information within another wirelesscommunication network, or transmitting a jamming signal to interferewith the network. Although the interferer 140 may be transmittinginformation, from the point of view of the second device 120, theinterferer 140 is a noise source.

Various methods of communicating in the presence of noise and/orinterference have been developed, including the use of multipletransmitters and/or multiple receivers at one or more of thecommunicating devices. For example, IEEE 802.11n is a proposed amendmentto the IEEE 802.11-2007 wireless networking standard to significantlyimprove network throughput over previous standards, such as 802.11b and802.11g, by using a multiple-input/multiple-output (MIMO) system.

Nevertheless, some existing communications system standards lack ofeffective and constructive methods for compensating for interferenceand/or noise. In particular, the unlicensed nature of the ISM(Industrial, Scientific, and Medical) bands often requires communicationdevices that use the ISM bands to tolerate interference from otherequipment. Interference from other devices becomes an issue as devicesare allowed to operate in the same band without pre-determinedfrequency, temporal, or spatial planning.

There have been several attempts to mitigate this issue via higher layerprotocols. For example, methods involving cooperative scheduling havebeen implemented to turn portions of the random access channel into acontrolled access channel. Also, some work has been done to show thattime domain signal processing can be used to mitigate the effects ofnarrowband interference. However, these methods have primarily focusedon mitigating interference on the data payload, without taking intoaccount how interference affects other parts of the receiver, e.g., theyassume ideal estimation for parameters such as synchronization,including tasks such as gain control, packet detection, carrierfrequency offset correction and channel estimation.

Moreover, in military applications, where protection against jamminginterference is a design criterion, there is now an increasing demandfor high speed communications. On the other hand, several commercialnetworks such as WiFi and wireless video distribution systems are usingunlicensed bands which are quickly becoming overcrowded. The ability ofa system to cancel interference in these conditions may affect itscommercial viability.

The use of multiple antennas is motivated in part by the fact that thechannel capacity of the resultant channel can scale linearly with thenumber of transmitters/receivers. Multiple-Input/Multiple-Output (MIMO)antenna systems also provide the user with additional degrees of freedomover traditional single antenna (SISO) systems to enable optimumtransmission and compensate for in-band interference in scatter richenvironments. It may be desirable to use multi-antenna techniques tocompensate for in-band interference in scatter rich environments. Somework has been done on MIMO based interference mitigation for cellularsystems. This work has focused on reducing interference from neighboringcells or users by coordinating transmissions either in time, space orfrequency. MIMO does not, however, provide a method for mitigatinginterference from a non-cooperative external device transmitting aninterfering signal, such as a jammer or a non-cooperative co-user of thefrequency band.

An iterative maximum likelihood (ML) based algorithm may be effective,but it is computationally expensive and may not be feasible for highrate modulations. A minimum interference method may offer goodperformance in some scenarios but degrades when the interference becomesweak. While these methods address channel estimation in the presence ofinterference, they likewise do not consider gain control, packetdetection or carrier frequency offset in the presence of anon-cooperative external devices.

The multiple antennas in MIMO systems can be used as a spatial filter tomitigate unwanted interferences. The most straightforward approach toprovide spatial interference mitigation is to introduce at the beginningof the receiver chain a multi-tap interference rejection matrix filter.The matrix taps of this filter can be chosen to provide a null in thedirections of the MIMO mulitpaths channels of the interferences.However, this approach has as least two drawbacks which make itimpractical. First, the estimation algorithms used to find the multitudeof spatial filter taps are complex and take a long time to convergewithin an acceptable residual error, which makes it inappropriate forpacket-based communications. Second, the spatial filter must operate atthe symbol rate, and the filter length must match the interferencechannels excess delay. This means that for each received symbol, a largenumber of matrix multiplications occur, making this approach toocomputing intensive for a practical implementation.

By contrast, using a single tap spatial filter, in accordance withaspects of embodiments disclosed herein, instead of the multi-tap filteraddresses both these problems. Embodiments of a single-tap spatialfilter can be effective in mitigating or reducing narrowbandinterference. FIG. 2 is a flowchart illustrating a process 200 formitigating interference in a wireless communication network usingmultiple antennas. The process 200 for mitigating interference can beperformed, for example, by a receiving device. In block 210, the processbegins by receiving a noise-and-interference signal comprising at leastnoise component and an interference component. It is preferable that thenoise-and-interference signal not comprise a component corresponding tothe signal of interest; however, the scope of the invention is notso-limited. In some embodiments, the noise-and-interference signal isfurther processed to identify the separate noise and/or interferencecomponents from other components. The noise-and-interference signal canbe received on a number of different antennas; therefore, thenoise-and-interference signal can include multiple sub-signals, eachsub-signal corresponding to a single antenna. Reception of thenoise-and-interference signal can be performed prior to the transmissionof the signal of interest by a transmitting device, or can be receivedin between transmissions by the transmitting device.

In block 220, the covariance matrix of the noise-and-interference signalis estimated. The noise-and-interference covariance matrix (R) can beestimated using a number of suitable methods. For example, thenoise-and-interference signal can be decomposed in time into a number ofshorter sub-signals. The outer product of each of these sub-signals canthen be taken and averaged to form an estimate of thenoise-and-interference covariance matrix (R). In other embodiments, thenoise-and-interference signal can be decomposed in frequency, e.g., viaa suitable transform such as the Fourier transform. In block 230, aspatial filtering matrix (W) is computed, or otherwise derived, based onthe estimated noise-and-interference covariance matrix (R). The spatialfiltering matrix (W) is, in some embodiments, a multi-antenna filter. Anumber of methods for determining the spatial filtering matrix aredisclosed herein, including eigenvector nullling, noise whitening,covariance matrix inversion, and covariance matrix inversion withdiagonal loading.

In block 240, a communication signal is received, the communicationsignal including the signal of interest, noise, and interference. Asdescribed above with respect to the noise-and-interference signal, thecommunication signal can be received on a number of antennas, andtherefore can include multiple sub-signals, each sub-signalcorresponding to a single antenna. In other embodiments, the signal ofinterest, embedded in noise and interference, is received, before thenoise-and-interference signal. In general, in different embodiments ofthe method, the steps performed in the blocks of FIG. 2 can be performedin different orders. In block 250, the spatial filtering matrix (W) isapplied to the communication signal to produce a filtered communicationsignal. Application of the spatial filtering matrix (W) to thecommunication signal can be a matrix-vector multiplication performed bysoftware running in a microprocessor. Alternatively, application of thespatial filtering matrix can be accomplished using hardware such as anadaptive filter. Other suitable techniques for applying the spatialfiltering matrix can be used. The filtered communication signal is then,in block 260, demodulated and/or decoded according to standard methods.

In block 270, it is determined if there is more data to be received. Ifnot, the process 200 ends. If there is more data to be received, theprocess moves to block 280, where it is determined whether thenoise-and-interference signal has changed. In some embodiments, when ananalog gain control (AGC) gain has changed, it is determined that thenoise-and-interference signal has changed. Other methods of determininga change in the noise-and-interference signal can also be used. When itis determined that the noise-and-interference signal has changed, theprocess restarts at block 210. However, when it is determined that thenoise-and-interference signal is substantially the same, the processreturns to block 240 to receive further communication signals. Someembodiments of the process lack block 280, and instead return to block210 when more data is to be received.

FIG. 3 is a functional block diagram of a receiver having aninterference mitigation module. The receiver 300 can implement theprocedure 200 described above and/or other interference mitigationprocedures. The receiver 300 includes a number of antennas 310. Withoutlimiting the number, the receiver can have two, three, four, or morethan four antennas. In some embodiments, the antennas are configured forboth reception and transmission of signals, whereas in otherembodiments, the antennas are only configured for receiving signals. Insome embodiments, the receiver 300 can have a different number oftransmitting antennas than receiving antennas. The antennas areelectrically coupled to a preliminary processor 320.

The receiver includes both a preliminary processor 320 and a secondaryprocessor 360. The preliminary processor 320 can include mechanisms forprocessing received signals prior to interference mitigation, and thesecondary processor 360 typically includes mechanisms for processing thefiltered communication signal. The preliminary processor 320 can includemodules for analog gain control (AGC), spatial, temporal, orfrequency-based filtering, such as bandpass or halfband filtering,and/or performing a Fourier or inverse Fourier transform. The secondaryprocessor 360 can include modules such as a demodulation module, aFourier transform or inverse Fourier transform module, a decoder modulefor decoding signals coded using an error-corrective code such as aHamming code, a convolutional code, a turbo code, or a low-densityparity check (LDPC) code, a deinterleaving module, and a demultiplexer.In other embodiments, modules which are listed as being associated withthe secondary processor 360 can be included in the preliminary processor320.

The receiver also includes an interference mitigation module 340 whichreceives data from the preliminary processor 320 over a data link 330,performs an interference mitigation procedure on the data received fromthe preliminary processor 320, and then transmits the filtered data tothe secondary processor 360 over a data link 350. The interferencemitigation module 340 can include a number of sub-components, asdiscussed in detail below.

The receiver can also include a microprocessor 370 and a memory 380. Themicroprocessor can be used by any of the other components, such as theprimary processor 320, the interference mitigation module 340, or thesecondary processor 360 to perform data calculations. As used herein,the term “processor” refers broadly to any suitable device, logicalblock, module, circuit, or combination of elements for executinginstructions. The microprocessor 370 can be any conventional generalpurpose single- or multi-chip microprocessor such as a Pentium®processor, Pentium II® processor, Pentium III® processor, Pentium IV®processor, Pentium® Pro processor, a 8051 processor, a MIPS® processor,a Power PC® processor, or an ALPHA® processor. In addition, theprocessor can be any conventional special purpose microprocessor such asa digital signal processor. The various illustrative logical blocks,modules, and circuits described in connection with the embodimentsdisclosed herein can be implemented or performed with a general purposeprocessor, a digital signal processor (DSP), an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA) orother programmable logic device, discrete gate or transistor logic,discrete hardware components, or any combination thereof designed toperform the functions described herein. A general purpose processor,such as microprocessor 370 can be a conventional microprocessor, but inthe alternative, the microprocessor 370 can be any conventionalprocessor, controller, microcontroller, or state machine. Microprocessor370 can also be implemented as a combination of computing devices, e.g.,a combination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other such configuration.

The memory 380 can also be connected to the other components of thereceiver, such as the preliminary processor 320, the interferencemitigation module 340, the secondary processor 360 and themicroprocessor 370. Memory refers to electronic circuitry that allowsinformation, typically computer data, to be stored and retrieved. Memorycan refer to external devices or systems, for example, disk drives ortape drives. Memory can also refer to fast semiconductor storage(chips), for example, Random Access Memory (RAM) or various forms ofRead Only Memory (ROM), which are directly connected to the one or moreprocessors of the receiver 300. Other types of memory include bubblememory and core memory.

The interference mitigation module 340 comprises a number ofsub-components, including a covariance estimation submodule 342, aspatial filter submodule 344, and a controller 346. The covarianceestimation submodule 342 and spatial filter submodule 344 both receivedata from the preliminary processor 320 over the data link 330. Both thecovariance estimate submodule 342 and the spatial filter submodule 344are electrically coupled to the controller 346 which controls thefunctions of the submodules.

The covariance estimation submodule 342 receives signals from theantennas 310 via the preliminary processor 320 and estimates thecovariance of the received signal. As described above, in oneembodiment, the covariance estimation submodule temporally splits thereceived signal into a number of sub-signals and averages the outerproduct of the sub-signals.

The spatial filter submodule 344 both generates and applies a spatialfilter to signals received from the antennas via the preliminaryprocessor 320, such as the communication signal comprising the signal ofinterest embedded in noise and interference. In some embodiments of thereceiver 300, the generation and application of the spatial filter areperformed in separate modules. For example, in one embodiment thegeneration of the spatial filter is performed by the microprocessor 380.In other embodiments, a dedicated spatial filter generation module canbe distinct from a spatial filter application module. Once thenoise-and-interference covariance matrix (R) has been estimated, e.g.,by the covariance estimation submodule 342, there are several methodsthat can be used to generate a spatial filtering matrix (W). Methodsdisclosed herein include eigenvector nulling, noise whitening,covariance matrix inversion, and covariance matrix inversion withdiagonal loading. Other methods of generating the spatial filteringmatrix can also be used.

As mentioned above, there are a number of methods for generating aspatial filtering matrix (W) based on the estimatednoise-and-interference covariance matrix (R). One such method,eigenvector nulling (or simply eigen nulling), involves placing a nullin the direction of the strongest eigenmode. One method of doing thisinvolves taking a eigendecomposition or singular value decomposition(SVD) of the estimated noise-and-interference covariance matrix (R) andreplacing the strongest eigenvector corresponding to the largesteigenvalue with the all-zeroes vector. The singular value decompositiontheorem states that any matrix, such as the estimatednoise-and-interference covariance matrix (R), can be factored asfollows:R=UΣV*,where U and V are unitary matrices, and E is an matrix with non-negativevalues along its diagonal. Assuming that R is a 4×4 matrix,corresponding to a receiver 300 with four antennas 310, this can furtherbe written as:

${R = {{U\;\Sigma\; V^{*}} = {{\left\lbrack {{{\begin{bmatrix}\; \\u_{1} \\\;\end{bmatrix}\begin{bmatrix}\; \\u_{2} \\\;\end{bmatrix}}\begin{bmatrix}\; \\u_{3} \\\;\end{bmatrix}}\begin{bmatrix}\; \\u_{4} \\\;\end{bmatrix}} \right\rbrack\begin{bmatrix}\sigma_{1} & \; & \; & \; \\\; & \sigma_{2} & \; & \; \\\; & \; & \sigma_{3} & \; \\\; & \; & \; & \sigma_{4}\end{bmatrix}}\begin{bmatrix}\left\lbrack v_{1} \right\rbrack \\\left\lbrack v_{2} \right\rbrack \\\left\lbrack v_{3} \right\rbrack \\\left\lbrack v_{4} \right\rbrack\end{bmatrix}}^{*}}},$where u₁, u₂, u₃, and u₄ are eigenvectors, and σ₁, σ₂, σ₃, and σ₄, arethe singular values. The singular values are sorted such thatσ₁>σ₂>σ₃>σ₄.

The spatial filtering matrix (W) can be derived from the matrix U, byreplacing the leftmost column with the all-zeros vector and conjugatingthe result. Thus, in some embodiments,W=[[0][u ₂ ][u ₃ ][u ₄]]*.

Eigenvector nulling has shown to be very effective when the interferencepower is high. It places a harsh null in the direction of the strongesteigenmode. However, as the interference power approaches the noisefloor, the harshness of the null may be detrimental to the performance.This may result from the main lobe of the strongest eigenmode becomingdistorted by the noise. This may also result in the sidelobes beinglarger. When the noise power is within 10 dB of the interference power,the covariance matrix may be less likely to optimally identify theinterference source with its strongest eigenvector. This may causesuboptimal nulling and, in some cases, may hinder performance. Heuristicschemes can be derived to disable interference mitigation usingeigenvector nulling when the interference power is low.

Another method of interference mitigation is noise whitening. Noisewhitening (or simply whitening) involves using the square root of theinverse of the estimated noise-and-interference covariance matrix as thespatial filter. The noise whitening approach provides an output signalwith substantially uncorrelated noise. A drawback of this approach isthat, in some applications, it can potentially amplify the noise.Preferably, the interference mitigation method should suppress theinterference power more than it amplifies then noise power.

Simulations have shown circumstances in which the noise whiteningapproach was unable to sufficiently suppress the interference power, andprovided only a marginal improvement when compared to results with nointerference mitigation at all. An alternative approach is to skip thesquare root and use the inverse directly. This method, referred to acovariance matrix inversion (or nulling), results in a suppression ofthe interfering signal which is much higher, with the drawback that theresidual noise will be correlated. Since the residual interference powermay in some circumstances be the limiting factor for performance, thereduction in its power may be more beneficial than the lack of whitenessin the noise signal.

Mathematically, the spatial filter (W) for noise whitening can bewritten as follows:

$W = {{{\left\lbrack {{{\begin{bmatrix}\; \\u_{1} \\\;\end{bmatrix}\begin{bmatrix}\; \\u_{2} \\\;\end{bmatrix}}\begin{bmatrix}\; \\u_{3} \\\;\end{bmatrix}}\begin{bmatrix}\; \\u_{4} \\\;\end{bmatrix}} \right\rbrack\begin{bmatrix}\frac{1}{\sqrt{\sigma_{1}}} & \; & \; & \; \\\; & \frac{1}{\sqrt{\sigma_{2}}} & \; & \; \\\; & \; & \frac{1}{\sqrt{\sigma_{3}}} & \; \\\; & \; & \; & \frac{1}{\sqrt{\sigma_{4}}}\end{bmatrix}}\begin{bmatrix}\left\lbrack v_{1} \right\rbrack \\\left\lbrack v_{2} \right\rbrack \\\left\lbrack v_{3} \right\rbrack \\\left\lbrack v_{4} \right\rbrack\end{bmatrix}}^{*}.}$The spatial filter (W) for nulling can be written as follows:

$W = {{{\left\lbrack {{{\begin{bmatrix}\; \\u_{1} \\\;\end{bmatrix}\begin{bmatrix}\; \\u_{2} \\\;\end{bmatrix}}\begin{bmatrix}\; \\u_{3} \\\;\end{bmatrix}}\begin{bmatrix}\; \\u_{4} \\\;\end{bmatrix}} \right\rbrack\begin{bmatrix}\frac{1}{\sigma_{1}} & \; & \; & \; \\\; & \frac{1}{\sigma_{2}} & \; & \; \\\; & \; & \frac{1}{\sigma_{3}} & \; \\\; & \; & \; & \frac{1}{\sigma_{4}}\end{bmatrix}}\begin{bmatrix}\left\lbrack v_{1} \right\rbrack \\\left\lbrack v_{2} \right\rbrack \\\left\lbrack v_{3} \right\rbrack \\\left\lbrack v_{4} \right\rbrack\end{bmatrix}}^{*}.}$

Inadequate estimation of the covariance matrix can lead to largesidelobes and a distorted mainlobe in the spatial gain pattern of thereceiver 300. In order to mitigate these effects, diagonal loading canbe applied to the estimated noise-and-interference covariance matrixprior to taking the inverse as described above. This method has beenshown to improve the stability of the resulting inverse. Diagonalloading involves adding a value to each diagonal element of theestimated noise-and-interference covariance matrix. This improves therank of the estimated noise-and-interference covariance matrix and thespatial gain pattern of the inverse that is calculated. One may see thegreatest benefit from this technique when the interference power isclose to the noise power. Diagonal loading reduces the depth of thenulls that are created. The diagonal loading added to the estimatednoise-and-filter covariance can be based on the noise power and can beany fraction of or greater than the noise power.

Other methods, such as methods based on combinations of the teachings ofthe disclosed embodiments, can also be used to determine the spatialfilter (W). The spatial filter (W) can be based, at least in part, onthe estimated noise-and-interference covariance matrix (R). The spatialfilter (W) can also or instead be based on a different covariancematrix, such as the covariance matrix of the communication signal or apredetermined noise-and-interference covariance matrix.

As stated above, both the covariance estimation submodule 342 and thespatial filter submodule 344 are provided to the controller 346 whichcontrols the functions of the submodules. The controller 346 is, in someembodiments, responsible for coordinating the transmission and receptionof data (such as matrices) between components of the receiver 300. Itcan also be responsible for ensuring that the noise-and-interferencecovariance matrix is not estimated while the signal of interest is beingtransmitted. Finally, the controller 346 may also ensure that thespatial filtering matrix is not updated while the receiver 300 isdecoding a packet.

In order to prevent estimation of the covariance matrix while the signalof interest is in transit, a protocol may be used that provide timeswhen the channel between two devices is free of a signal-of-interestcomponent. For example, by guaranteeing an interframe spacing (IFS), theprotocol can ensure that the receiver 300 has enough time to estimatethe noise-and-interference covariance matrix between packets. In someembodiments, a protocol guarantees an IFS of at least 1 microsecond,about 50 microseconds, between 10 and 100 microseconds, or anothersuitable IFS that provides enough time for covariance matrix estimationto occur. The controller 346 can also keep track of settings from withinthe preliminary processor 320, such as the gain setting of an analoggain control unit. If the gain changes during an estimation period, thiscan be factored into the estimation of the noise-and-interferencecovariance matrix. In one embodiment, the estimation is restarted. Inother embodiments, the data is weighted according to the gain setting atthe time of recording, but this is complicated by the transient dynamicsof the analog gain control unit. While estimating the covariance of thenoise-and-interference signal, key control signals from the receiver 300can also be observed, such as a control signal indicating a packet hasbeen detected.

In order to have a robust interference mitigation subsystem anotherprotocol for handling loss of synchronization with the transmitter hasbeen developed. If the covariance matrix is estimated while the signalof interest is being transmitted, or if the covariance estimate isotherwise faulty, the signal of interest could potentially be removed.To prevent this failure condition, a timeout period is used as afailsafe mechanism. If no packet is detected after a given amount oftime, the controller 346 can enter a timeout state. While in this statethe controller 346 waits for a fixed period of time between computingestimates of the noise-and-interference covariance. The microprocessor380 can continue to process the covariance estimates and returnfiltering matrices. The controller 380 can continue to operate intimeout mode until another packet is detected. The amount of timerequired to recover from this type of failure depends on the ratio ofthe length of the packet (in microseconds) and the length of theinterframe spacing. If the transmission times are chosen randomly, thenthe probability of n successive failed covariance estimates and theprobability of a successful covariance estimate can be determined fromthe equations that follow:

$P_{fail} = \frac{T_{packet}}{T_{IFS} - T_{estimation}}$P_(failed_covariance) = P_(fail)^(n), n = trailsP_(succesful_covariance) = 1 − P_(failed_covariance) = 1 − P_(fail)^(n)

The state diagram of an embodiment of a controller 346 that performs theoperations described above is shown in FIG. 4. The oval states are thestates during which the controller 346 allows for updating of thespatial filtering matrix from the microprocessor 380. The states labeled“Update R” are the states during which the controller 346 can pass a newmatrix to the microprocessor 380. This state machine ensures thecovariance matrix is computed during interframe spacings withoutfluctuations from the analog gain control unit. It also ensures that thespatial filtering matrix is not updated while a packet is being decoded,e.g., by the secondary processor 360.

The controller 346 begins in state 405, in which it waits for anestimation stimulus. The estimation stimulus is any event recognizableby the controller 346 to trigger estimation of the covariance matrix.The estimation stimulus can be derived from a waveform received on theantennas 310, or the estimation stimulus can be a control signal fromthe microprocessor 380. For example, the event can be the detection of apacket, or the detection of a packet with specific information in aheader thereof. Alternatively, the event can be the reception of asignal from the microprocessor 380 indicating that estimation of thecovariance matrix should occur. The microprocessor 380 can determinewhen to transmit such a signal based on any of a number of factors, suchas when a packet has been detected or when it is determined that packetsare no longer being detected. Once the stimulus is detected, thecontroller 346 moves to state 410 in which it waits a certain amount oftime, such as, for example, a predetermined amount of time sufficient toensure the channel is free of a signal-of-interest component, e.g., atleast 1 microsecond, between about 1 microsecond and about 100microseconds, 5 microseconds, an amount of time determinedalgorithmically, or another suitable amount of time, before moving tostate 415. The controller 346 can be configured to wait another suitableamount of time before moving to state 415. In state 415, the controller346 instructs the covariance estimation submodule 342 to estimate thenoise-and-interference covariance matrix based on a received waveform.The waveform can be associated with a reception time, e.g., the periodof time over which the waveform was received on the antennas 310. Due tofinite processing speed, the time at which the covariance matrixsubmodule 342 obtains the waveform can be different from the receptiontime, which corresponds to when the waveform is received on the antennas310. If the controller 346 determines that the analog gain controlsetting has changed during the reception time, the controller 346 canreset the current estimate in state 420 and instruct the covarianceestimation submodule 342 to restart estimation of thenoise-and-interference covariance matrix based on a new waveform with anew reception time. Similarly, if the controller 346 determines that apacket has been detected during the reception time, the controller 346can reset the current estimate of the covariance matrix in state 430.

Once the noise-and-interference covariance matrix is estimated, thecontroller 346 enters an idle state 425 in which it waits to detect apacket or to timeout. Due to finite processing speed, there is a delaybetween the reception time of the waveform and when a packet is detectedin the waveform. Thus, the controller waits an appropriate delay orinterval before determining that a packet (or signal of interestcomponent) is not present in the received waveform on which thecovariance matrix estimate is based. If a packet is detected during idlestate 425, the controller enters a state 430 in which thenoise-and-interference covariance matrix is reset. If the controller 346times out from state 425, it enters a state 435 in which it instructsthe microprocessor 380 or the spatial filter module 344 to update thespatial filtering matrix. Both states 430 and 435 return the controller346 to the start state 405.

If an estimation stimulus is not detected, or if a faulty estimationoccurs, as described above, the controller 346 enters a state 440 inwhich it instructs the covariance estimation submodule 342 to estimatethe noise-and-interference covariance matrix based on a receivedwaveform associated with a reception time. As above, if the controller346 detects that the analog gain control settings have changed duringthe reception time, the controller 346 resets the current estimate in areset state 445 and instructs the covariance estimation submodule 342 torestart estimation of the noise-and-interference covariance matrix basedon a new received waveform with a new reception time. If the controller346 determines that a packet has been detected during the receptiontime, the controller 346 returns to the start state 405.

After the noise-and-interference covariance matrix has been estimated,the controller 346 enters an idle state 450 in which it waits to detecta packet or timeout. If a packet is detected, the controller returns tothe start state 405. If the controller 346 times out from state 450, itenters a state 455 in which it instructs the microprocessor 380 orspatial filter module 344 to update the spatial filtering matrix beforechanging to a state 460 in which the controller 346 waits an amount oftime before returning to the start state 405.

Simulation and Implementation

The systems and methods disclosed above can be implemented usingsuitable hardware, software, and/or a combination of hardware andsoftware. A discussion of the results of empirical tests using theabove-discussed systems and methods is now presented.

A simulation of the systems and methods disclosed herein was performedusing a model of a transmitter compliant with the current IEEE 802.11nstandard, channel models disclosed by the IEEE 802.11 standard, and amodel of the receiver as discussed above. The simulation was performedusing MATLAB produced by The MathWorks, Inc. The transmitted data wasrandomly generated and encoded with a rate 1/2 convolutional code (133₈,171₈). In cases where the transmission scheme called for more than onespatial stream, the encoded data was spread across the streams in asystematic manner to take advantage of spatial diversity.

The encoded data on each spatial stream was then interleaved and mappedinto a quadrature amplitude modulation (QAM) constellation. Theinterleaver depth was matched to the number of encoded bits that wasmodulated onto each orthogonal frequency division multiplex (OFDM)symbol. The constellation used in the simulation was 16-QAM. Each ofthese constellation points was used to modulate 52 data subcarriers inthe OFDM system. The modulation was performed with a 64-point FFT (FastFourier Transform), after which the resulting signal was extended with a16 sample cyclic prefix. When added to a OFDM modulation scheme, thecyclic prefix helps combat the effects of a multipath channel andreduces inter-carrier interference. The resulting OFDM symbols were thenwindowed and concatenated to generate the data payload of thetransmitted packet, which was sampled at 20 MHz.

In order to test the performance of some of the disclosed multi-antennainterference mitigation algorithms disclosed above, simulations wereperformed in which single tone jammer was randomly placed in the band ofinterest. Both the jammer and the transmitted packet were passed throughspatial channels and combined at the receiver. The resultant signal wassubjected to an interference mitigation algorithm and demodulated anddecoded according to current standards.

The transmitted signal and the interfering source were convolved withrandomly generated channels. These channels were modeled after thechannels described in the IEEE 802.11 TGn Draft proposal, in whichchannels A, B, and D were used. Channel A is a signal-tap Rayleigh flatfading channel, channel B is a frequency-selective channel with 15 nsrms delay spread, and channel D is a frequency-selective channel with 50ns rms delay spread. These channels are typical of what would be seen ina home environment. In some embodiments, the amount of delay spread hasa dramatic affect on the performance of the system.

The transmitted waveform was an 802.11n compliant OFDM signal. Thus, itused the same short and long training sequences for time and frequencysynchronization. In the simulation, an HT (High Throughput) mixed modepacket format was used for all packets. Channel estimation was doneusing the HT long training sequences. The data payload of the packetswas 100 bytes.

A quiet period was inserted before each packet which the receiver usedto estimate the noise-and-interference covariance matrix (R). Theestimation was performed using 1000 samples. Once thenoise-and-interference covariance matrix (R) had been estimated, variousmethod were used to generate the spatial filtering matrix (W). Thereceived signal (y_(dirty)) was multiplied by spatial filtering matrix(W) to generate the filtered version of the signal(V_(clean)=Wy_(dirty)). The filtered signal was then passed into astandard-compliant 802.11n receiver. The decoded data was compared withthe transmitted data to determine if the packet was received correctly.Packet error rate is calculated based on the output of the receiver.

As an initial measure of performance, simulations were performed toinvestigate the gain in SINR. To perform this measurement, instead ofcombining the data signal and the interference signal, the spatialfiltering matrix was applied to each signal individually. The signalpower and interference power were measured before and after theapplication of the spatial filtering matrix. The ratio of these powerswas averaged over 1000 trials. The signal-to-interference ratio (SIR)was swept from −20 dB to +15 dB for channels A, B, and D at asignal-to-noise ratio (SNR) of 10 dB, 15 dB, 20 dB, and 25 dB.

FIGS. 5A-5L are plots of the simulated signal-to-noise-plus-interferenceratio (SINR) gain of the results. FIGS. 5A-5C show the results of thesimulation using an eigenvector nulling interference mitigation methodfor channels A, B, and D. FIG. 5D-5F show the results of the simulationusing a covariance matrix inversion interference mitigation method usingno diagonal loading for channels A, B, and D. FIGS. 5G-5I show theresults of the simulation using the covariance matrix inversion methodwith −10 dB of diagonal loading, and FIGS. 5J-5L show the results of thesimulation using the matrix inversion method with +10 dB of diagonalloading.

The results show that the covariance matrix inversion method withoutsome amount of diagonal loading may cause a decrease in the SINR whenthe interference power is low (see, e.g. FIGS. 5D-5L). As the amount ofdiagonal loading is increased, a floor develops at high SIR. This floormay represent the fact that the total SINR is dominated by the noisepower as opposed to the interference power. In this scenario, thesimulations show no appreciable gain in SINR.

Another factor in the performance of a communications system issynchronization. In order to properly decode an OFDM waveform, thereceiver may benefit from synchronizing in time with the OFDM symbolboundary. Once the receiver is synchronized, it can further correct forcarrier frequency change that could occur due to frequency offsetbetween the transmitter and receiver local oscillators. Once it hascorrected for these effects, the receiver can also estimate the channel.The presence of an interfering signal can have detrimental effect onparameter estimations. In order to test the performance of some of themitigation techniques, a simulation was performed with the lowest ratemodulation, binary phase shift keying (BPSK), with a rate 1/2 codetransmitted on a single spatial stream. The simulation determined if thepacket was detected and the header data correctly decoded.

FIGS. 6A-6F are plots showing the packet detection performance of thevarious methods in channels A, B, and D. FIGS. 6A-6C show the resultsfor channels A, B and D in 10 dB SNR, whereas FIGS. 6D-6F show theresults for the same channels in 15 dB SNR. The following table is alegend for the curves of FIGS. 6A-6F.

TABLE 1 Legend for Packet Error Rate Curves N No interference mitigationS Eigenvector nulling W Covariance matrix inversion without diagonalloading −10 dB Covariance matrix inversion with −10 dB diagonal loading 0 dB Covariance matrix inversion with 0 dB diagonal loading  10 dBCovariance matrix inversion with 10 dB diagonal loading

The performance of the covariance matrix inversion based methods wassimilar in all channels, but the eigenvector nulling clearly showed abetter preference in non-dispersive channels. The simulation shows thatthe eigenvector nulling method outperformed the other methods when thedelay spread in the channel was relatively small.

Similar multi-antenna interference mitigation methods were tested in thepresence of a wideband interference source. In this case, the simulationagain shows that the channel may have a significant impact on theperformance of the mitigation algorithm. FIGS. 7A-7C show the results ofthe simulation for channels A, B, and D. The performance of the singletap system in channel A, which is a single-tap Rayleigh flat fadingchannel, is reasonably good, however, once the channel becomes moredispersive, the simulations show that a single spatial tap may not beenough to cancel the interference. The simulations show that in anon-dispersive channel, the single spatial tap performs relatively welleven at very low SIR, however, once the channel becomes more dispersive(see, e.g., FIGS. 7B and 7C) the performance quickly degrades at lowerSIR. The previously mentioned heuristic scheme for disabling theinterference mitigation helps to solve this issue. Again, thesimulations show the harshness of covariance matrix inversion may bedetrimental when the performance of the system is limited by noise poweras opposed to interference power.

Simulations were also performed to determine the packet error rate(PER). FIGS. 8A-8I are plots showing the results of a packet error ratesimulation for various channels for a various number of transmitterantennas at 20 dB SNR. FIGS. 8A-8C show the results of the PERsimulation for channels A, B, and D using 1 transmitter antenna. FIGS.8D-8F show the results of the PER simulation for the same channels using2 transmitter antennas. FIG. 8G-8I show the results of the PERsimulation for the same channels using 3 transmitter antennas.

The simulations show that the frequency diversity present in themultipath channel has a beneficial effect on the performance. They alsoshow that the eigenvector nulling method has a poor performance in thehigh SIR region of the plots. This may be due to the lack ofintelligence in the receiver to disable the interference mitigation whenthe interference power has dropped well below the noise floor. Themethods examined have similar performance in the low SIR (−20 dB)region. The performance varies for each algorithm as the SIR isincreased from −20 dB to +40 dB. The simulations show that thecovariance matrix inversion method has almost no benefit when theinterference power is equal to or less than the noise power. Asdiscussed previously, one drawback of covariance matrix inversionwithout diagonal loading is that it can potentially amplify the noisepower. If the noise power is amplified more than the interfering signalis suppressed, there should be no distinguishable improvement inperformance. The diagonal loading improves performance in this region bybiasing the noise-and-interference covariance matrix. The simulationsshow that as the diagonal loading is increased, the performance in the 0dB to 20 dB region is similarly increased. These gains are achievablewith little or no loss in the low SIR region from −20 dB to 0 dB. Thesimulations also show that when the interference power is sufficientlybelow the noise floor, the performance of the diagonally loadedcovariance matrix inversion tracks the performance of the non-filteredsignal, whereas the other methods actually have performed worse in thisregion.

Channel D provides the best performance, possibly due to the nature ofOFDM modulation, rather than the nature of the interference. Channel Dhas a 50 ns rms delay spread, which creates a frequency-selectivechannel. This frequency diversity is exploited by the coding andinterleaving that is performed over the data subcarriers. Channel A is asingle tap Rayleigh flat fading channel. Since the simulated OFDMmodulation scheme cannot exploit frequency diversity in this channel,there are more drastic results when the packet is transmitted through abad channel. Channel B has 15 ns rms delay spread, so it is not asselective as channel D, but it does provide some amount of frequencydiversity. The increase in performance is apparent from the y-axis ofFIGS. 8A-8C.

The same simulations were run with 2 and 3 transmit antennas. The PERcurves generally have the same basic shape, with the main differencebeing the overall performance (see, e.g., FIGS. 8D-8I).

Some of the disclosed multi-antenna mitigation schemes can beimplemented on hardware. For example, the hardware can be combined witha MIMO OFDM cognitive radio testbed, such as, for example, a cognitiveradio testbed developed and available from Silvus Technologies, Inc. ofLos Angeles, Calif. In some embodiments, the transmitter and receiver ona testbed are contained in a field-programmable gate array (FPGA), in aprogrammable logic device, or in another suitable type of circuit. Aninterference mitigation module can be added functionally before theexisting receiver to filter the received signal and mitigateinterference before the existing receiver attempts to decode the packet.

The interference mitigation module, similar to the embodiment shown inFIG. 3, comprised four major logical components. The first of the fourcomponents was a covariance estimation block for estimating a covariancematrix based on a received waveform. This was performed as an averagingof the outer product of the received waveform.

The second component was responsible for calculation of the spatialfiltering matrix. For practical reasons, this operation was done on amicroprocessor that was attached to the FPGA which contains thereal-time MIMO OFDM transceiver. As this was being done on amicroprocessor, it was done in full-floating point arithmetic usingmatrix inversion and SVD decomposition algorithms, all of which can bewritten in a computer programming language, such as, for example C orC++. The third component performed the matrix-vector multiplicationrequired to apply the spatial filter. The final component was acontroller which executed a finite state machine similar to that shownin FIG. 4.

A protocol was developed for passing matrices between the host and FPGAto prevent data corruption. The typical interval between passing thecovariance matrix to the host and receiving a spatial filtering matrixback was roughly 1 millisecond. The matrices were double-buffered withinthe FPGA to ensure the interference mitigation subsystem did not attemptto read a matrix before the data had been successfully received from themicroprocessor. A similar protocol was used to ensure data integrity onthe covariance matrices sent to the microprocessor.

Without additional processing, a single-tap spatial filter isineffective at suppressing wideband interference in dispersive channelsdue to the frequency selectivity present in dispersive channels. Becausethe frequency response of the channel is not constant across the entireband, unwanted attenuation occurs outside of the flat range of thefilter. In certain embodiments, a filter that doesn't use a flat channelresponse is implemented in order to mitigate the wideband interference.For example, the signal may be broken into sub-band components and theinterference in each sub-band may be individually mitigated. If eachsub-band is narrow enough, the channel in the sub-band may approximate afrequency flat channel. Individual mitigation of sub-bands could beperformed according to any appropriate means, including the techniquesdiscussed herein.

Sub-banding could be done using polyphase filter banks, but they may becomputationally expensive. Various transforms or filters may be used toseparate the incoming signal into its sub-band components. In certainembodiments, the Discrete Fourier Transform (DFT), which is a lineartransformation, is used to separate the incoming signal into itssub-band components. While this transform has an efficient hardwareimplementation, it creates filters with relatively high side-lobes.These side-lobes may allow energy from neighboring sub-bands to leakinto the primary sub-band.

Windows, such as the Hamming window, can be applied to data before thetransform to suppress the side-lobes. While these may be effective insuppressing the energy in the side-lobes, they tend to widen the mainlobe causing the effective sub-bands to become wider. The bandwidth ofthe sub-band is a key factor in the validity of the narrowbandassumption used to drive the filter-bank approach. To improveeffectiveness, the bandwidth of the sub-band is generally less than thecoherence bandwidth of the channel. Another window that may beconsidered is the Rife-Vincent window which has a narrower main-lobe atthe cost of slightly higher side-lobes. Embodiments may incorporate anysuitable window or windows, including but not limited to, rectangularwindows, Hann windows, Tukey windows, cosine windows, or any othersuitable windows.

FIG. 9A is a flowchart illustrating a method for mitigating interferencein a wireless communication network using multiple antennas. Asindicated by 9A-1, the method includes determining a respective spatialfilter for each of a number of sub-bands that the wideband channel hasbeen divided into. In one embodiment each respective spatial filter is asingle tap spatial filter. As indicated by 9A-2, the method includesreceiving at least one signal including a signal-of-interest (i.e.information), noise and/or interference. As indicated by 9A-3, themethod includes converting the at least one received signal into arespective number of frequency sub-band components. As indicated by9A-4, the method includes applying the respective spatial filters to thecorresponding frequency sub-band component of the at least one receivedsignal to obtain filtered frequency sub-band components.

FIG. 9B is a flowchart illustrating a method for mitigating interferencein a wireless communication network using multiple antennas. Asindicated by 9B-1, the method includes receiving at least one firstsignal assumed or known to include noise and/or interference without asubstantial signal-of-interest. As indicated by 9B-2, the methodincludes determining a respective spatial filter for each of a number ofsub-bands that the wideband channel has been divided into using the atleast one first signal. In one embodiment each respective spatial filteris a single tap spatial filter. As indicated by 9B-3, the methodincludes receiving at least one second signal assumed or known toinclude a signal-of-interest (i.e. information), noise and/orinterference. As indicated by 9B-4, the method includes converting theat least one second signal into a respective number of frequencysub-band components. As indicated by 9B-5, the method includes applyingthe respective spatial filters to the corresponding frequency sub-bandcomponents of the at least one second signal to obtain filteredfrequency sub-band components. As indicated by 9B-6, the method includesconverting the filtered frequency sub-band components into at least onethird signal in the time domain.

FIG. 9C is a flowchart illustrating a method for mitigating interferencein a wireless communication network using multiple antennas. Asindicated by 9C-1, the method includes converting at least one signalassumed or known to include noise and/or interference and assumed orknown to not include a substantial signal of interest into a respectivenumber of frequency sub-band components. As indicated by 9C-2, themethod includes using the frequency sub-band components to determine acovariance matrix for each sub-band. As indicated by 9C-3, the methodincludes inverting each respective covariance matrix to determine arespective spatial filter for each sub-band.

FIG. 9D is a flowchart illustrating a method for mitigating interferencein a wireless communication network using multiple antennas. Asindicated by 9D-1, the method includes converting at least one signalassumed or known to include noise and/or interference and assumed orknown to not include a substantial signal of interest into a respectivenumber of frequency sub-band components. As indicated by 9D-2, themethod includes using the frequency sub-band components to determine acovariance matrix for each sub-band. As indicated by 9D-3, the methodincludes determining the square root of the inverse of the covariancematrix to obtain the corresponding spatial filter for a sub-band.

FIG. 9E is a flowchart illustrating a method for mitigating interferencein a wireless communication network using multiple antennas. Asindicated by 9E-1, the method includes converting at least one signalassumed or known to include noise and/or interference and assumed orknown to not include a substantial signal of interest into a respectivenumber of frequency sub-band components. As indicated by 9E-2, themethod includes using the frequency sub-band components to determine acovariance matrix for each sub-band. As indicated by 9E-3, the methodincludes weighting a respective identity matrix for each sub-band. Asindicated by 9E-4, the method includes adding the respective identitymatrix to the corresponding estimated covariance matrix to produce arespective diagonally loaded covariance matrix for each sub-band. Asindicated by 9E-5, the method includes inverting each respectivediagonally loaded covariance matrix to determine a respective spatialfilter for each sub-band.

FIG. 9F is a flowchart illustrating a method for mitigating interferencein a wireless communication network using multiple antennas. Asindicated by 9F-1, the method includes converting at least one signalassumed or known to include noise and/or interference and assumed orknown to not include a substantial signal of interest into a respectivenumber of frequency sub-band components. As indicated by 9F-2, themethod includes using the frequency sub-band components to determine acovariance matrix for each sub-band. As indicated by 9F-3, the methodincludes decomposing each covariance matrix into at least one unitarymatrix and at least one diagonal matrix. As indicated by 9F-4, themethod includes setting at least one column in a respective unitarymatrix to the all zero vector. As indicated by 9F-5, the method includesinverting each respective diagonally loaded covariance matrix todetermine a respective spatial filter for each sub-band.

The systems and methods disclosed above can be implemented usingsuitable hardware, software, and/or a combination of hardware andsoftware. A discussion of the results of empirical tests using theabove-discussed systems and methods is now presented.

A simulation of the systems and methods disclosed herein were evaluatedusing a model comprising a MATLAB (The MathWorks, Inc.) model of a MIMOOFDM transmitter, Rayleigh flat fading and frequency selective channelmodels, and a model of the entire receiver complete with all estimationand synchronization loops. The user data was encoded with a rate 1/2convolutional code (133₈, 171₈). When the transmission scheme called formore than one spatial stream the encoded data was spread across thestreams to exploit spatial diversity.

The encoded data on each spatial stream was then interleaved and mappedinto a quadrature amplitude modulation (QAM) constellation. Theinterleaver depth was matched to the number of coded bits that will bemodulated onto each orthogonal frequency division multiplex (OFDM)symbol. A 16-QAM constellation was used to modulate the data onto eachsubcarrier. The modulation was performed by a 64-point FFT (Fast FourierTransform), after which the resulting signal was extended with a 16sample cyclic prefix. The resulting OFDM symbols were then windowed andconcatenated to generate the data payload of the transmitted packet,which was sampled at 20 Mhz.

The transmitted waveform was a MIMO OFDM signal with the appropriatenumber of spatial streams. Training sequences were used for time andfrequency synchronization as well as channel estimation. Each packet wastransmitted with a 100 byte data payload.

The receiver performed time and frequency synchronization with theincoming packet to align the Fourier transform with the OFDM symbolboundary and to compensate for carrier frequency offset. It alsoestimated the channel for each subcarrier using training sequences. AMMSE MIMO decoder used channel estimates and demodulated signal toestimate the transmitted constellation points. These points were thende-mapped, de-interleaved and decoded using standard techniques. Thissystem is similar to the one used in the narrowband interferencemitigation simulation described above, with the primary difference beingthat the single-tap spatial filter was replaced with a filter-bank.

In certain embodiments, an overlap and add technique is integrated in aMIMO receiver to provide spatial interference mitigation for widebandchannels. With reference to FIG. 10, the wideband filter-bank employed aseries of overlapping DFT operations to perform the sub-banding andfiltering. Consecutive samples of the incoming signal were transformedvia the DFT operation and filtered in the frequency domain. Thesetransforms were taken every D samples, where D represents the Short-timeFourier Transform (STFT) interval 1012. For a 32-point DFT with an8-sample STFT interval, a new 32-DFT point is computed every 8 samples.Each sub-band was filtered using the sub-band filtering matrices afterwhich the inverse DFT transformed the signal back into the time-domain.Samples from overlapping DFT operations 1024 were averaged to generatethe filtered time-domain signal. When the simulation parameters calledfor them, windows were applied to the input of the DFT and output of theinverse DFT operations. The windows were matched in length to the sizeof the DFT.

FIG. 11A is a simplified block diagram of an embodiment of aSingle-Input-Multiple-Output (SIMO) system 1100 a in which multi-antennainterference mitigation procedures such as those disclosed above may beimplemented. The system 1100 a includes a single transmitter antenna1110 and four receiver antennas 1130 a, 1130 b, 1130 c, 1130 d. Whilefour receiver antennas 1130 a, 1130 b, 1130 c, 1130 d are shown, thoseskilled in the art will appreciate that a SIMO system may include two ormore receiver antennas.

The system also includes a Discrete Fourier Transform (DFT) module 1135,a covariance matrix estimation module 1150, a spatial filter calculationmodule 1160, a spatial filter module 1170 and a demodulator 1190. Thoseskilled in the art will appreciate from the disclosure herein that theDFT module 1135, the covariance matrix estimation module 1150, thespatial filter calculation module 1160, the spatial filter module 1170and the demodulator 1190 are each more complex than what is illustrated.Those skilled in the art will also appreciate that the system 1100 awould contain more elements than are shown FIG. 11A, and FIG. 11A onlyincludes those elements beneficial to describing certain aspects ofembodiments specific to SIMO systems.

In operation, the single transmitter antenna 1110 and four receiverantennas 1130 a, 1130 b, 1130 c, 1130 d create independent frequencyselective channels 1128. Moreover, the methods performed by the DFTmodule 1135, the covariance matrix estimation module 1150, the spatialfilter calculation module 1160, and the spatial filter module 1170 canbe used to reduce the impact of interfering signals transmitted fromjammer 1115, which may be a wideband or narrowband jammer. Both thejammer 1115 and the transmitter antenna 1110 transmit signals that arereceived by the receiver antennas 1130 a, 1130 b, 1130 c, 1130 d. Thesesignals are combined at the receiver antennas 1130 a, 1130 b, 1130 c,1130 d and passed through the interference mitigation algorithm, whichoperates as disclosed herein. This filtered signal 1180 was then fedinto the receiver.

FIG. 11B is a simplified block diagram of an embodiment of aMultiple-Input-Single-Output (MISO) system 1100 b in which multi-antennainterference mitigation procedures such as those disclosed above may beimplemented. The system 1100 a includes four transmitter antennas 1110a, 1110 b, 1110 c, 1110 d and a single receiver antenna 1130. While fourtransmitter antennas 1110 a, 1110 b, 1110 c, 1110 d are shown, thoseskilled in the art will appreciate that a MISO system may include two ormore transmitter antennas.

The system 1100 b also includes a Discrete Fourier Transform (DFT)module 1135, a covariance matrix estimation module 1150, a spatialfilter calculation module 1160, a spatial filter module 1170 and ademodulator 1190. Those skilled in the art will appreciate from thedisclosure herein that the DFT module 1135, the covariance matrixestimation module 1150, the spatial filter calculation module 1160, thespatial filter module 1170 and the demodulator 1190 are each morecomplex than what is illustrated. Those skilled in the art will alsoappreciate that the system 1100 b would contain more elements than areshown FIG. 11B, and FIG. 11B only includes those elements beneficial todescribing certain aspects of embodiments specific to MISO systems.

In operation, the single receiver antenna 1130 and four transmitterantennas 1110 a, 1110 b, 1110 c, 1110 d create independent frequencyselective channels 1128. Moreover, the methods performed by the DFTmodule 1135, the covariance matrix estimation module 1150, the spatialfilter calculation module 1160, and the spatial filter module 1170 canbe used to reduce the impact of interfering signals transmitted fromjammer 1115, which may be a wideband or narrowband jammer. Both thejammer 1115 and the transmitter antennas 1110 a, 1110 b, 1110 c, 1110 dtransmit signals that are received by the receiver antenna 1130. Thesesignals are combined at the receiver antenna 1130 and passed through theinterference mitigation algorithm, which operates as disclosed elsewhereherein. This filtered signal 1180 was then fed into the receiver.

FIG. 11C is a simplified block diagram of an embodiment of aMultiple-Input-Multiple-Output (MIMO) system 1100 c in whichmulti-antenna interference mitigation procedures such as those disclosedabove may be implemented. The system 1100 c includes a four transmitterantennas 1110 a, 1110 b, 1110 c, 1110 d and four receiver antennas 1130a, 1130 b, 1130 c, 1130 d. While four transmitter antennas and fourreceiver antennas are shown, those skilled in the art will appreciatethat a MIMO system may include two or more transmitter antennas and twoor more receiver antennas.

The system 1100 c also includes a Discrete Fourier Transform (DFT)module 1135, a covariance matrix estimation module 1150, a spatialfilter calculation module 1160, a spatial filter module 1170 and ademodulator 1190. Those skilled in the art will appreciate from thedisclosure herein that the DFT module 1135, the covariance matrixestimation module 1150, the spatial filter calculation module 1160, thespatial filter module 1170 and the demodulator 1190 are each morecomplex than what is illustrated. Those skilled in the art will alsoappreciate that the system 1100 b would contain more elements than areshown FIG. 11B, and FIG. 11B only includes those elements beneficial todescribing certain aspects of embodiments specific to MIMO systems.

In operation, the four transmitter antennas 1110 a, 1110 b, 1110 c, 1110d and four receiver antennas 1130 a, 1130 b, 1130 c, 1130 d createindependent frequency selective channels 1128. Moreover, the methodsperformed by the DFT module 1135, the covariance matrix estimationmodule 1150, the spatial filter calculation module 1160, and the spatialfilter module 1170 can be used to reduce the impact of interferingsignals transmitted from jammer 1115, which may be a wideband ornarrowband jammer. Both the jammer 1115 and the four transmitterantennas 1110 a, 1110 b, 1110 c, 111 transmit signals that are receivedby the receiver antennas 1130 a, 1130 b, 1130 c, 1130 d. These signalsare combined at the receiver antennas 1130 a, 1130 b, 1130 c, 1130 d andpassed through the interference mitigation algorithm, which operates asdisclosed elsewhere herein. This filtered signal 1180 was then fed intothe receiver.

As discussed above, the inter-frame spacing between the packets was usedto estimate the covariance matrix R of the interference+noise signal.Since this interval will have the interfernce but not the packet, it isideal for covariance matrix estimation. Since the testing environmenthad a 4-antenna receiver, the estimated covariance matrix was a 4×4matrix. Once the covariance has been estimated there are several methodsthat can be used to generate a spatial filtering matrix. For generality,with regard to FIG. 11, this matrix is referred to as W.

Several of these methods were discussed above. In certain embodiments itmay be advantageous to implement nulling with some amount of diagonalloading. As discussed above, this method involves adding a constantproportional to the noise power to the main diagonal of the covariancematrix. For convenience and desirability based on performanceevaluations, embodiments described herein focus on this method. However,other methods of generating a spatial filtering matrix, including butnot limited to those described above, may also be used.

In the present simulation, the received signal 1140 was broken down intoits sub-band components by the DFT based filter bank. Each sub-bandcomponent was filtered by the appropriate W for that sub-band togenerate the filtered version these sub-band components. This filteredsignal was fed into the MIMO OFDM receiver. The decoded data wascompared with the transmitted data to determine if the packet wasreceived correctly. Packet error rate was calculated based on the outputof the receiver.

Since wideband interference may be difficult to suppress in moredispersive channels, all simulations were run in a Rayleigh frequencyselective channel with 50 ns rms delay spread. To demonstrate how muchinterference can be suppressed, the distribution of the eigen-values ofthe interference in each sub-band was analyzed. The ratio of thedominant eigen-value to the rest of the eigen-values provides an insightinto how much interference can be suppressed by removing that eigen-modefrom the incoming signal. This is discussed in more detail below. Afterdemonstrating that the filter-bank approach is able to suppress someinterference, the suppression ability was measured by evaluating thegain in the Signal to Interference+Noise Power, as well as the packetdetection and overall packet error rate performance. These results arepresented below. In to demonstrate the tradeoff between DFT size andrequired null-depth, we investigated the affect the filter structure ofchoice had on the incoming signal. The effective channel that thereceiver sees in the signal is the convolution of the actual channelwith the filter. Finally, to demonstrate how much interference power wassurviving the filtering operation and to demonstrate how much distortionthe filter was creating in the signal, the losses from the filteringoperation were analyzed, and are discussed below.

Eigen-value Distribution

As an initial measure of potential interference suppression ability, theeigen-value distribution of the covariance matrices for each sub-bandwas analyzed. Since the interference is from a single source, the powerin the covariance matrix is dominated by a single eigen-mode. Thisindicates efficient energy compaction and allows creation of a deepernull without sacrificing as many degrees of freedom. The spatialcovariance in each sub-band was measured, as was the ratio (in dB) ofdominant eigen-value to the 2^(nd), 3^(rd), and 4^(th) eigen-values.Each trial consisted of convolving the interference with a randomlygenerated channel and measuring the spatial covariance in each sub-band.The statistics were collected over 1000 trials, with DFT sizes rangingfrom 16 to 512 samples. The cumulative density functions (CDF) for theratios of the 2^(nd) and 3^(rd) eigen-values to the dominant are shownin FIG. 14A.

FIG. 14A demonstrates that the larger DFTs may provide greaterseparation between the dominant eigen-mode and the secondaryeigen-modes. The 512 pt DFT shows the best energy compaction with thesecond eigen-value 25-30 dB lower than the primary. While this may bedesirable, it is associated with a degree of computational complexity.In certain embodiments, it may be desirable to find a smaller DFT whichstill achieves reasonable energy compaction. In certain embodiments,windows may be useful for modifying the filter response of the DFT. Incertain embodiments, windows may change the properties of theeigen-value distribution, and may provide increased potential forinterference suppression.

The Hamming window and the Rife-Vincent windows were considered for theanalysis portion of the filter bank. The Hamming window is a window withlow sidelobes but a large main lobe. The Rife-Vincent has a narrowermain lobe at the cost of higher side lobes. FIG. 13 shows the frequencyresponse of the Hamming and Rife-Vincent windows. This tradeoff betweenmain-lobe width and side-lobe rejection will be discussed in more detailbelow.

FIG. 14B shows the improvement that can be achieved by using windows tosuppress the side-lobes. The noiseless case is also presented to showthe maximum performance that can be achieved with a rectangular window.At 30 dB there is no notable loss from the noise. The other linesrepresent the CDF of the eigen-value ratio for the Rife-Vincent windowand the Hamming window. These CDFs are 8 dB lower than that of therectangular window, and therefore it may be possible to suppress anadditional 8 dB of interference power without sacrificing another degreeof freedom.

As explained above, the Rife-Vincent window has a narrower main-lobethan the Hamming window at the cost of higher side-lobes. Both windowsshow similar ability to suppress interference. Since the computationalcomplexity associated with both windows is similar, both are consideredthroughout the discussion below. FIG. 14C shows the performance ofseveral DFT sizes with the Hamming window. This figure shows that afilter-bank based on 32 pt DFTs or longer may be sufficient to suppress20 dB interference.

SINR Gain

Before attempting to demodulate any packets it may be valuable to knowhow much the interference power is being suppressed with respect to thesignal power. As an indicator of potential performance, the gain inSignal to Interference+Noise Ratio (SINR) was analyzed. This measurementwas performed by using the same method described above. The signal powerand interference power were measured before and after the application ofthe DFT-based filter bank. The ratio of these powers was averaged over1000 trials. Each trial consisted of a single packet being transmittedin the presence of interference. The SIR was swept from −20 dB to +20dB. This was done in a Rayleigh frequency selective channel with 50 nsrms relay spread and with SNR ranging from 10 dB to 25 dB.

FIG. 15 shows the gain in SINR for various DFT sizes with standardrectangular analysis and synthesis windows. The gains from the largerDFT are evident when the interference power is significantly strongerthan the signal power. For instance when the interference power is 20 dBgreater than the signal power, under certain conditions, the 128 pt DFTcan provide approximately 9 dB more suppression than the 16 pt DFT. Asthe interference power gets weaker the smaller DFTs are able to suppressthe interference approximately as well as the larger ones. The solid setof curves corresponds to 10 dB SNR, while the dashed curves correspondto 15 dB SNR. These curves are separate in the range of 5 dB foressentially all SIR, which may indicate that the interference can besuppressed to a certain level but not below that. The gains achieved bythe larger DFT are also less dramatic when the SNR is lower.

Packet Error Rate

The discussion above demonstrates that the algorithm of certainembodiments suppresses the interference power relative to the signal ofinterest. Before directly analyzing the packet error rate of thesimulation system, the discussion below first demonstrates that thefactor driving the packet error rate is the bit error rate of the userdata. Synchronization may be a major factor in the performance of acommunications system. In order to properly decode an OFDM waveform, itmay be required to synchronize the receiver in time with the OFDM symbolboundary. If synchronization fails too easily, its failure rate maydominate the overall performance of the system. This may create anundesirable floor on the overall performance of the system.

To ensure that these estimation tasks are not limiting the systemperformance, we evaluated the packet detection performance. Packetdetection failure may occur if the header containing the modulationparameters is incorrectly decoded. FIG. 16A shows the packet detectionperformance as a function of SIR for various DFT sizes with rectangularwindows. Once again, the solid curves correspond to 10 dB SNR and thedashed curves are with 15 dB SNR. As demonstrated in FIG. 16A, that theerror rates are fairly low when the SNR is at least 15 dB. The largerDFTs improve the packet detection performance when the interferencepower is high. As the interference power decreases the error rates ofthe various DFT sizes converge to give roughly the same performance.

It was determined that the packet error rate of the system will not belimited by the performance of packet detection and synchronization. Therelevant DFT sizes provided enough interference suppression forreasonable performance when the SNR was greater than 15 dB. FIG. 16Bdemonstrates the packet error rate performance of the various DFT sizesfor a single spatial stream packet modulated with a 16-QAM constellationand encoded with a rate 1/2 convolutional code. These packets were 100bytes in length.

FIG. 16B shows a tradeoff between the interference suppression abilityof the larger DFT with the inter-symbol interference (ISI) generated bythe larger filter length. When the interference is very strong thegreater interference suppression ability of the larger DFTs may providethe gain in SINR required for successful demodulation. For example, at−20 dB SIR the 128 pt DFT provided the best performance. As theinterference power decreases, the dispersion resulting from the longfilter length may begin to dominate the performance and an error floormay develop. At the far right of FIG. 16B, the error rates for the fourDFT sizes flatten out and create an error floor. The region from −10 dBto +5 dB SIR shows the trade-space for the DFT size with respect to theinterference power. At −10 dB, the 64 pt DFT began to provide betterperformance than the 128 pt DFT; then at +5 dB, the 32 pt DFT providedbetter performance than both of the larger DFTs. Since the smaller DFTshave less dispersion they create lower error floors. According to FIG.16B, when sweeping the SIR from −10 dB to +5 dB, the benefit of thelower error floors begin to take effect.

The results from FIG. 16B were generated using rectangular windows onthe analysis and synthesis sections of the filter bank. As demonstratedabove, windows may improve the ability of the filter-bank to suppressinterference by creating a larger separation between the dominanteigen-value and the secondary eigen-values. FIG. 16C shows how windowingeffects overall packet error rate performance for a 32 pt DFT basedfilter bank.

As a baseline we evaluated the performance of the system without anyinterference mitigation. This is shown in the solid curve. Once againthe solid curve represents 10 dB SNR while the dashed curve represents15 dB SNR. We tested the filter bank with rectangular, Rife-Vincent, andHamming analysis windows. The performance of these analysis windows isshown in the curves with the circle, Xs and squares, respectively. Thesetrials were run with rectangular synthesis windows. According to FIG.16C, even though the Rife-Vincent window has a narrower main-lobe, itdidn't perform as well as the Hamming window. This may be due to thehigher side-lobes. Since the Hamming analysis window gave the bestperformance gain, we applied it to the synthesis portion of the filterbank where we saw an additional improvement in performance (hamm₂). Thebit error rate for the same set of simulations is shown in FIG. 16D.

FIG. 16E shows the performance with multiple spatial streams with thewideband mitigation filter at 15 dB SNR. The higher rate modes didn'tperform as well as the single spatial stream transmission. This is to beexpected since the effective receiver diversity is reduced by thespatial filtering performed by the filter-bank. The solid lines refer tothe performance of the unmitigated system, while those with the circlesrefer to system with interference mitigation. The loss of diversity isdemonstrated by the shallowness of the slope of the mitigated curves. Ifthe diversity order was higher the fall off would have been steeper.

Filter Loss

In FIG. 16B, there is a visible cross-over region where the system withinterference mitigation performed worse than the un-mitigated system.This was due to distortion and dispersion in the signal created by theinterference mitigation filter. FIG. 17 shows how the filter affects theperformance of our system. The performance of the filter bank withrectangular windows and identity matrices for each sub-band were tested.Since the DFT is a lossless transform, these parameters should configurethe filter to return the exact same signal as we passed into the filter.The simulations showed that this was in fact the case, and that theperformance was the same as the unmodified system. The interference wasthen used to train the filter. This meant estimating the covariance foreach sub-band using the method described and applying the inverse in theappropriate sub-band. These were used to filter a clean signal in theabsence of interference. The purpose of this was to see how the signalof interest is affected by the filter.

FIG. 17 shows a 3-4 dB loss due to the filter distortion. The solidcurve represents the unmodified system operating in the absence ofinterference. The curves with squares and diamonds represent the systemwith the filter enabled. The set of solid curves corresponds to 0 dBSIR, the broken ones had 5 dB SIR, and the dashed curves represent 20 dBSIR. The curves with diamonds correspond to the performance wheninterference was introduced to the received signal and passed throughthe filter. The additional loss from the interference was 0.4-0.6 dB. Itappears that most of the distortion comes from the filter-bank itself,and that the interference itself is being suppressed. Distortion fromthe filter-bank may be due to the loss in degrees of freedom and ISIcreated by the long filter length. The stronger the interference power,the more the filter will have to distort the incoming signal to suppressthe interference. FIG. 17 shows that the filter distortion loss wasgreater when the interference power was stronger. The filter was stillsuppressing the interference, which is demonstrated by the fact that theadditional loss from the interference was only 0.5 dB.

The discussion above demonstrates that a single tap spatial filter maynot be enough to mitigate wideband interference sources. In certainembodiments, a filter-bank approach separates the signal into sub-bandcomponents and filters each sub-band individually. In certainembodiments, filter-banks may be built from polyphase filters or usingstandard transforms such as the Discrete Fourier Transform. Thediscussion above demonstrates how a DFT based approach can be used tomitigate wideband interference. In certain embodiments, the length ofthe DFT used for the filter-bank impacts the amount of interference thatthe filter bank can suppress. A longer DFT may be capable of suppressingstronger interference, but may result in increased dispersion. As theinterference power becomes weaker the cost of this dispersion mayoutweigh the enhanced suppression provided by the longer DFT. Thediscussion above demonstrates that the smaller DFTs may have lower errorfloors and shows the trade-space where this may become the drivingfactor for performance. Finally, the discussion above analyzes thefilter dispersion, providing insight into what characteristics of theimpulse response are indicative of good performance.

FIG. 12 is a block diagram of an embodiment of a filter-bank system thatmay be used to implement the algorithms discussed abover. A bank of FFTs(e.g., 1220, 1222) operate in parallel to compute the short-time Fouriertransforms. In certain embodiments, a 32-point DFT with an 8-sampleoverlap may be used for this implementation. Such an embodiment wouldmean building 4 FFTs in parallel for the analysis portion of the filterbank. Each FFT is paired with a module that applies the analysis window1210 to the incoming signal. In certain embodiments, parallel inverseFFTs (IFFTs) are used to transform the frequency domain signal back tothe time-domain before applying the synthesis windows (e.g., 1270) andcombining the signals for the receiver. In certain embodiments, thespatial filtering matrix (e.g., 1240, 1242) is applied to each sub-bandbetween the FFT and IFFT. In certain embodiments, these matrices arecomputed by a host power-pc processor and transferred to theinterference mitigation engine.

In certain embodiments, a controller similar to the one described abovewith reference to narrowband mitigation may be used. In certainembodiments, the covariance is estimated by taking the output of thefirst analysis FFT 1220 and averaging the outer-product for eachsub-band. In some embodiments, a special unit manages the estimation ofthis matrix and transfers to the host processor.

A realtime system incorporating the features of the embodiments justdescribed was used to implement the algorithms described above. Thesystem was used to characterize the performance of 16-QAM OFDM packetsas a function of SIR. The signal power was calibrated to be 17 dB abovethe noise seen at the receiver. The interference power was then sweptwith respect to the signal power. One hundred byte packets weretransmitted using 1, 2, and 3 spatial streams. The source data wasprotected by a rate 1/2 convolutional code before being mapped onto16-QAM constellation points for each subcarrier. The packet error ratewas measured for each mode for each SIR. This was initially done withoutthe multi-antenna interference mitigation filter, and then again withthe filter enabled. The results are shown in. FIG. 18. The hardwaretestbed was able to suppress the interference to roughly the level ofthe noise. FIG. 18 shows that at 17 dB SNR, the performance of the oneand two spatial stream modes are roughly the same from −15 dB SIR to 20dB SIR. The interference was suppressed to the level of the noise floorthroughout this entire region. The loss of degrees of freedom can beseen by noting that the three spatial stream mode could not bedemodulated until the interference power dropped below the noise. Theeigen-analysis above showed that we two degrees of freedom would have tobe sacrificed to suppress the interference by 20 dB. This is confirmedby the relatively poor performance of the 3-spatial stream mode and goodperformance of the 1 and 2-spatial stream modes.

Frequency Domain Interference Cancellation in Wideband Multi-AntennaSystems

In certain embodiments, rather than using spatial processing on asub-carrier basis in the OFDM demodulator, a plug-and-play approach maybe used, wherein an interference cancelling module is simply inserted inthe receiver chain. In certain embodiments, such an approach mayadvantageously improve the packet acquisition performance in thepresence of interference, as it may mitigate interference during theacquisition process. In addition, the plug-and-play property may provideeasy integration with different technologies, and may reduceinter-carrier leakage.

Again, in certain embodiments, the OLA technique is integrated in a MIMOreceiver to provide spatial interference mitigation for widebandchannels.

FIG. 19 is a flowchart of an embodiment of a frequency-domain widebandinterference mitigation system. In the embodiment of FIG. 19, a module1920 is inserted in front of a conventional MIMO receiver 1940 whichperforms wideband interference mitigation through a transformation tothe frequency domain and back to the time domain. In certainembodiments, apart from the operating bandwidth, there is no directrelationship between the MIMO receiver and the MIMO frequency domaininterference cancellation module, and any type appropriate MIMO receivercan be used (e.g., packet acquisition, MIMO OFDM for WiFi, MIMO OFDM forWiMAX, MIMO CDMA, or any other appropriate receiver.) In certainembodiments, the interference cancellation module is transparent to theconventional MIMO receiver. That is, the receiver 1940 will not see adifference between the received signals 1930 from the N_(ri) outputs ofthe interference cancellation module versus the signals received fromN_(ri) antennas as in any MIMO system, except that the inserted modulehas mitigated the interference, which may greatly improve theconventional receiver performance without any specific action on thepart of the receiver.

In certain embodiments, the steps of the frequency domain interferencecancellation described are as follows: (1) Get the time domain signalsfrom Nr receive antennas; (2) perform, through an OLA operation, spatialfiltering in the frequency domain on the received signals to mitigatethe wideband interference sources; and (3) generate an interference freetime domain signal with Nri branches (this signal is seen by theconventional MIMO OFDM receiver as a time domain signal coming from Nrireceive antennas.)

FIG. 20 provides additional detail of the major blocks of the OLA basedfrequency domain interference cancellation module as illustrated in FIG.19. The OLA engine operates every R input samples. That is, if the inputsymbol rate is R_(in), then the OLA engine implements all its functions(extraction, windowing, FFT, multiplication, IFFT and output bufferaddition) within R/R_(in) seconds. The parameters of the OLA engine areas follows: R—input and output sliding window step size; N_(w)—ConstantOvelap and Add (COLA) window length (any window with the COLA propertycan be chosen; note that the step size and window length are chosenjointly as a function of the window function to preserve the COLAproperty;) N_(f)—FFT length; N_(r)—number of receive antennas;N_(ri)—number of parallel data stream after interference cancellation.

At initialization, n_(in) is set equal to 0, n_(out) is set equal tozero and the output buffer is initialized with zeros. The first N_(w)-RN_(r)x1 incoming data vectors are put in the input buffer. Note that foradded efficiency, the input and output buffers can be implementedcircularly. The different operations in the OLA engine are thefollowing: (1) The next R N_(r)x1 incoming data vectors are put in theinput buffer, after which, at block 2010, N_(W) data vectors areextracted from the input buffer from position n_(in) to n_(in)+N_(w)-1and n_(in) is incremented by R; (2) the N_(W) extracted data vectors aremultiplied vector wise by the N_(W) point COLA window at block 2020 andare zero padded with N_(f)-N_(W) N_(r)x1 zero vectors at block 2030,after which the N_(f) points vector FFT is taken on the windowed andzero-padded data sequence at block 2040; (3) for each of the N_(f)sub-carrier, the N_(r)x1 received data vector Y_(f) is right multipliedby a N_(ri)xN_(r) matrix H_(f), at block 2050, to obtain theinterference mitigated N_(ri)x1 vector X_(f)—the N_(f) matrices H_(f)are chosen according to the interference spatial filtering algorithm(spatial whitening, interference null space projection, correlationmatrix inversion with diagonal loading, etc.) selected by the designer;(4) the IFFT of the N_(f) N_(ri)x1 X_(f) vectors is then taken at block2060 and the N_(f) N_(ri)x1 vectors are vector wise accumulated at block2070 to the data in the output buffer from position n_(out) ton_(out)+N_(f)-1 (it is also possible to multiply the data by an outputwindow prior to the accumulation to the buffer). The vectors fromposition n_(out) to n_(out)+R-1 are outputted from the OLA engine andtransferred to the next receiver module. The output buffer point n_(out)is finally incremented by R.

In certain embodiments, an algorithm will use an estimate of Rf theinterference autocorrelation on each subcarrier in the frequency domainin order to compute the N_(f) H_(f) matrices. FIG. 21 provides a blockdiagram of a method for estimating frequency domain interferenceautocorrelation matrices. As illustrated in Error! Reference source notfound.21, the N_(f) Rf matrices are computed following windowing,zero-padding and the FFT to correctly estimate the interference spatialstatistics following the FFT (i.e., the location where interferencemitigation takes place inside the OLA engine.)

In certain embodiments, the N_(f) matrices H_(f) can be computed using awhitening interference mitigation approach. FIG. 22 provides a blockdiagram of such an approach. Note that the last three steps, 2250, 2260and 2270 (transformation to the time domain using the IFFT, time domainwindowing and FFT to the frequency domain) are accomplished to limit theequivalent impulse response length of interference mitigation filter anddecrease the signal distortion that might be introduced by the circularconvolution artifacts of the OLA engine.

The previous algorithm can be built on hardware using a 32-point FFTwith a 24 sample overlap. Hamming windows can be applied to both sides,as explained above, which may improve performance. A hardwarearchitecture embodiment for this algorithm was provided in FIG. 12, anddetailed explanation of the architecture is provided above withreference thereto.

Testing of the algorithm was conducted using a hardware embodiment asprovided in FIG. 12. Our first design goal was to provide interferencemitigation in wideband multi-antenna systems. FIGS. 23A and 23Billustrate the packet error rate (PER) performance of an entire 802.11nsystem integrated with the frequency domain interference cancellationsystem described above. The OLA engine parameters were Hanning COLAwindow of length Nw=91 points, a sliding window step size of R=46 and aFFT size of Nf=128 points. The spatial filtering matrices were computedusing the whitening approach and the impulse response of theinterference mitigation filter was windowed using a 31 points Hammingwindow. The full featured 802.11n modem included modules such as packetsynchronization, channel estimation, MMSE MIMO detection, etc. FIGS. 23Aand 23B demonstrate that the impact of wideband interference wasmitigated even with a signal to interference ration (SIR) as severe as−20 dB.

FIG. 23C compares the performance of the OLA engine with frequencymitigation performed after the FFT within the OFDM demodulator (curvesdenoted with Freq.). Note that in this case we assumed perfect packetsynchronization. The advantages of the algorithm are demonstrated in thefigure. The impact of packet synchronization is on a single tap timedomain filter is illustrated in FIG. 23D. Therefore, the differencebetween an OFDM interference mitigation approach and an OLA engineapproach may be even more dramatic, since, as illustrated in FIG. 23D,the packet detection rate may be unacceptable at low SIR with a singletap time domain filter.

Finally, from a complexity point of view, with the current set ofparameters we performed approximately 2.75 matrix multiplication perinput symbol. Other simulations have shown that we can reduce this ratiodown to 2 without significantly affecting the performance. Compared tothe OFDM based interference mitigation, we also performed an additionalvector FFT and a vector IFFT. Thus the complexity was slightlyincreased; however the performance was greatly improved.

In certain embodiments implementing a frequency domain interferencecancellation approach, therefore, the following advantages, amongothers, may be achievable: (1) desirable interference mitigation inwideband channels; (2) improved packet synchronization; (3)plug-and-play property; and (4) low complexity to enable hardwarerealization.

Conclusion

While the above description has pointed out novel features of theinvention as applied to various embodiments, the skilled person willunderstand that various omissions, substitutions, and changes in theform and details of the device or process illustrated may be madewithout departing from the scope of the invention. Therefore, the scopeof the invention is defined by the appended claims rather than by theforegoing description. All variations coming within the meaning andrange of equivalency of the claims are embraced within their scope.

Reference throughout this specification to “some embodiments” or “anembodiment” means that a particular feature, structure or characteristicdescribed in connection with the embodiment is included in at least someembodiments. Thus, appearances of the phrases “in some embodiments” or“in an embodiment” in various places throughout this specification arenot necessarily all referring to the same embodiment and may refer toone or more of the same or different embodiments. Furthermore, theparticular features, structures or characteristics can be combined inany suitable manner, as would be apparent to one of ordinary skill inthe art from this disclosure, in one or more embodiments.

As used in this application, the terms “comprising,” “including,”“having,” and the like are synonymous and are used inclusively, in anopen-ended fashion, and do not exclude additional elements, features,acts, operations, and so forth. Also, the term “or” is used in itsinclusive sense (and not in its exclusive sense) so that when used, forexample, to connect a list of elements, the term “or” means one, some,or all of the elements in the list.

Similarly, it should be appreciated that in the above description ofembodiments, various features are sometimes grouped together in a singleembodiment, figure, or description thereof for the purpose ofstreamlining the disclosure and aiding in the understanding of one ormore of the various inventive aspects. This method of disclosure,however, is not to be interpreted as reflecting an intention that anyclaim require more features than are expressly recited in that claim.Rather, inventive aspects lie in a combination of fewer than allfeatures of any single foregoing disclosed embodiment.

Embodiments of the disclosed systems and methods can be used and/orimplemented with local and/or remote devices, components, and/ormodules. The term “remote” may include devices, components, and/ormodules not stored locally, for example, not accessible via a local bus.Thus, a remote device may include a device which is physically locatedin the same room and connected via a device such as a switch or a localarea network. In other situations, a remote device may also be locatedin a separate geographic area, such as, for example, in a differentlocation, building, city, country, and so forth.

Although described in the illustrative context of certain preferredembodiments and examples, it will be understood by those skilled in theart that the disclosure extends beyond the specifically describedembodiments to other alternative embodiments and/or uses and obviousmodifications and equivalents. Thus, it is intended that the scope ofthe claims which follow should not be limited by the particularembodiments described above.

What is claimed is:
 1. A method of suppressing wideband interference ina wireless communication system, the method comprising: i.) receiving afirst signal on a frequency band, wherein the first signal comprisescomponents from a plurality of sub-channels; ii.) converting the firstsignal into a respective plurality of first sub-band frequencycomponents, wherein each sub-band is defined in the frequency domain;iii.) determining a respective single-tap spatial filter for eachfrequency sub-band using one or more corresponding first sub-bandcomponents for each respective spatial filter; iv.) receiving a secondsignal on said frequency band, wherein the second signal comprisescomponents from the plurality of sub-channels; v.) converting the secondsignal into a respective plurality of second sub-band frequencycomponents; and vi.) generating a corresponding plurality of filteredsub-band components by applying the respective single-tap spatialfilters to the corresponding second sub-band components of the secondsignal, wherein step iii) is performed before any of the steps iv)-vi);and wherein step iii) is performed without determining that the firstsignal includes a substantial signal of interest.
 2. The method of claim1, wherein step ii) is performed before any of the steps iii)-vi) andwherein step ii) is performed without determining that the first signalincludes a substantial signal of interest.
 3. The method of claim 1,further comprising determining whether one of the first or secondreceived signals includes the signal of interest.
 4. The method of claim3, wherein determining whether one of the first or second receivedsignals includes the signal of interest comprises detecting a packet. 5.The method of claim 1, further comprising receiving an estimationstimulus, wherein step iii) is triggered at least partially based on theestimation stimulus.
 6. The method of claim 5, wherein the estimationstimulus is included in one of the first or second signals.
 7. Themethod of claim 5, wherein the estimation stimulus comprises a controlsignal.
 8. The method of claim 5, wherein the estimation stimuluscomprises a determination that a packet was detected.
 9. The method ofclaim 5, wherein the estimation stimulus comprises a determination thatpackets are no longer being detected.
 10. The method of claim 1, whereinthe bandwidth of the sub-band frequency components is less than thecoherence bandwidth of the sub-channel.
 11. The method of claim 1,wherein step i) is performed without determining that the first signalincludes a substantial signal of interest.
 12. Non-transitory physicalcomputer storage comprising computer-executable instructions that, whenexecuted by a computing system, are configured to: i.) receive a firstsignal on a frequency band, wherein the first signal comprisescomponents from a plurality of sub-channels; ii.) convert the firstsignal into a respective plurality of first sub-band frequencycomponents, wherein each sub-band is defined in the frequency domain;iii.) determine a respective single-tap spatial filter for eachfrequency sub-band using one or more corresponding first sub-bandcomponents for each respective spatial filter; iv.) receive a secondsignal on said frequency band, wherein the second signal comprisescomponents from the plurality of sub-channels; v.) convert the secondsignal into a respective plurality of second sub-band frequencycomponents; and vi.) generate a corresponding plurality of filteredsub-band components by applying the respective single-tap spatialfilters to the corresponding second sub-band components of the secondsignal, wherein step iii) is performed before any of the steps iv)-vi);and wherein step iii) is performed without determining that the firstsignal includes a substantial signal of interest.
 13. Non-transitoryphysical computer storage of claim 12, wherein step ii) is performedbefore any of the steps iii)-vi) and wherein step ii) is performedwithout determining that the first signal includes a substantial signalof interest.
 14. Non-transitory physical computer storage of claim 12,wherein the computer-executable instructions are further configured todetermine whether one of the first or second received signals includesthe signal of interest.
 15. Non-transitory physical computer storage ofclaim 12, further comprising machine executable instructions storedthereon to receive an estimation stimulus, wherein step iii) istriggered at least partially based on the estimation stimulus. 16.Non-transitory physical computer storage of claim 15, wherein theestimation stimulus is included in one of the first or second signals.17. Non-transitory physical computer storage of claim 15, wherein theestimation stimulus comprises a determination that a packet wasdetected.
 18. Non-transitory physical computer storage of claim 15,wherein the estimation stimulus comprises a determination that packetsare no longer being detected.
 19. Non-transitory physical computerstorage of claim 15, wherein the bandwidth of the sub-band frequencycomponents is less than the coherence bandwidth of the sub-channel. 20.Non-transitory physical computer storage of claim 12, wherein step i) isperformed without determining that the first signal includes asubstantial signal of interest.
 21. A system for suppressing widebandinterference, the system comprising: an antenna configured to receivewireless signals; and a processing unit configured to: i.) process afirst signal on a frequency band received at the antenna, wherein thefirst signal comprises components from a plurality of sub-channels; ii.)convert the first signal into a respective plurality of first sub-bandfrequency components, wherein each sub-band is defined in the frequencydomain; iii.) determine, with the assumption that the first signal doesnot include a substantial signal of interest, a respective single-tapspatial filter for each frequency sub-band using one or morecorresponding first sub-band components for each respective spatialfilter; iv.) process a second signal on said frequency band received atthe antenna, wherein the second signal comprises components from theplurality of sub-channels; v.) convert the second signal into arespective plurality of second sub-band frequency components; and vi.)generate a corresponding plurality of filtered sub-band components byapplying the respective single-tap spatial filters to the correspondingsecond sub-band components of the second signal, wherein step iii) isperformed before any of the steps iv)-vi).
 22. The system of claim 21,wherein the processing unit is further configured to determine whetherone of the first or second received signals includes the signal ofinterest.
 23. The system of claim 21, wherein step i) is performedwithout determining that the first signal includes a substantial signalof interest.